Imagine a SimCity That Mirrors Your City
Imagine being so detail-oriented that you play a version of SimCity that perfectly replicates your city, not just the population size, but each neighborhood, each family, and even the same number of kids per block. The map matches real streets, and when your Sims commute to work, they get stuck in the same traffic jams you and I do.
While this scenario sounds like an urban planning dream (or nightmare), it also illustrates a breakthrough idea in media measurement: using synthetic populations to bridge the gap between two fundamentally different systems - panel-based data and census-level digital data.
Why Media Measurement Needs a Bridge
Today's media ecosystem is split in two:
- Analogue media relies on panels, representative samples of the population.
- Digital media relies on census-level tracking, one-to-one data on every user, where possible.
Each system speaks its own language. Digital's "track everyone" model doesn't align with analogue's sampling approach, and neither can fully extend into the other's ecosystem.
That's why marketers are still stuck comparing GRPs (Gross Rating Points) to impressions when trying to combine television and digital video.
The Power of a Synthetic Population
A synthetic population is like a panel at full census scale. There's one synthetic person for every real person.
Instead of relying on exact personal data, each synthetic individual mirrors the characteristics and behaviors of real people within a defined cohort. These populations can be used to simulate "day in the life" activities, like commuting or media consumption, to model how audiences move through channels in a privacy-safe way.
Key advantages:
- Localized intelligence
- Interoperability across platforms and media types
- Privacy protection - no personal data needed
Building a Foundation for the Future of Media
To make future media measurement scalable and sustainable across Canada and the U.S., cohort definitions must be:
- Publicly defined - transparent and consistent so all parties can align on shared standards.
- Platform-agnostic - usable across any system, ensuring interoperability between digital and analogue ecosystems.
- Future-proof - resilient to technological shifts, regulatory changes, and evolving consumer consent frameworks.
Both countries already have strong foundations to build on:
- In Canada, government census data and postal codes provide a stable, granular geographic framework for creating synthetic populations.
- In the United States, the Census Bureau's demographic data and ZIP codes offer similar stability and national coverage.
Together, these frameworks position North America to lead the way in building a unified, interoperable, and accurate media ecosystem - one that connects local insights to national understanding and enables truly scalable measurement.
From Measurement to Growth: The Marketer's View
Marketers aren't just measuring for measurement's sake - their goal is brand growth. That's why the focus must shift toward:
- Incrementality - proving that marketing drives growth
- Marketing Mix Modeling (MMM) - understanding how to allocate spend optimally
To reach these outcomes, the planning, buying, delivery, and reporting systems in media need to be aligned from the ground up. Synthetic populations provide that shared foundation.
Key Takeaways
- Synthetic populations combine the best of panel-based and census-level data, providing a unified view of media consumption.
- Canada's postal code and census foundation make it an ideal environment for developing interoperable, privacy-safe measurement systems.
- Incrementality and Marketing Mix Modeling (MMM) are essential for proving and optimizing marketing's impact on brand growth.
- Future media ecosystems must be built on public, interoperable cohort definitions that can evolve alongside technology and regulation.
- Cross-media measurement can finally become as seamless as digital reporting once synthetic populations are fully integrated into the media supply chain.
